Xinyue Gong | Geophysics | Best Researcher Award

Ms. Xinyue Gong | Geophysics | Best Researcher Award

PhD student at Zhejiang University , China

Xinyue Gong 🎓 is a dedicated and dynamic Ph.D. candidate at Zhejiang University, specializing in Resource Exploration and Geophysics. With a strong academic foundation from Ocean University of China, where she ranked 2nd in her class, she has consistently demonstrated intellectual curiosity and a passion for scientific inquiry. Her journey through the world of geosciences has been marked by an integration of advanced technologies such as deep learning, seismic data analysis, and remote sensing. 💻🛰️ Beyond her academic excellence, Xinyue has led and participated in multiple national innovation projects, showcasing her leadership, coding fluency, and creative visualization skills in platforms like Unity3D. 🌊 Her research strives to bridge theory and application, particularly in the reconstruction of sparsity seismic data using AI models like DnCNN and Diffusion Models. With a blend of technical brilliance and vision, Xinyue is poised to make impactful contributions to the future of geophysics and Earth observation. 🌍🚀

Professional Profile 

Scopus Profile

🎓 Education 

Xinyue Gong’s educational path is paved with precision and passion. 🧭 She is currently pursuing her Ph.D. in Resource Exploration and Geophysics at Zhejiang University, guided by Prof. Shengchang Chen. Her academic focus includes seismic inversion, computational geophysics, and AI-enhanced data processing—courses that anchor her deep understanding of the Earth’s subsurface. 🌐 Prior to her doctoral studies, she earned her Bachelor’s degree in Geo-information Science and Technology from Ocean University of China, graduating with a stellar GPA of 3.71/4.00 and securing the 2nd rank in her class of 34 students. 📘📈 This foundational training equipped her with both theoretical insight and hands-on skills in geospatial data, GIS, and remote sensing technologies. Her solid academic performance reflects not only her analytical prowess but also her unwavering commitment to the pursuit of knowledge. 🔬📚 From the oceans of Qingdao to the labs of Hangzhou, Xinyue’s academic journey is a story of vision and discipline.

💼 Professional Experience 

Xinyue Gong’s professional pursuits revolve around the intelligent integration of geophysical concepts with modern AI techniques. 🧠🌋 As a doctoral researcher, her primary project involves the reconstruction of seismic data by blending deep learning and knowledge-driven constraints. She has tackled the challenges of spatial irregularity and theoretical limitations in compressed sensing with cutting-edge models like DnCNN and Diffusion Models. 📡 Her hands-on experience extends back to her undergraduate days, where she led a national innovation project on coastline detection using deep learning techniques such as FCN and HED. She also contributed as a core member in developing a visual simulation for Ocean Bottom Seismograph (OBS) deployment using Unity3D and C#, enhancing the interactive understanding of seismic operations. 🛠️🖥️ Her combined exposure to algorithm development, simulation, and real-world geoscience applications makes her a versatile and forward-thinking researcher, capable of transforming complex earth systems into computationally navigable frameworks. 🚧🔍

🔬 Research Interest 

Xinyue Gong’s research compass points toward the frontier of AI-driven geoscience. 🧭🧠 Her interests are anchored in the acquisition and processing of sparsity seismic data, where she seeks to overcome limitations in conventional reconstruction through advanced algorithms. With a deep appreciation for the power of compressed sensing and the interpretability of deep learning, she explores hybrid models that combine data-driven methods with domain knowledge—an approach evident in her work with DnCNN and Diffusion Models. 🌀🧩 She is equally intrigued by the use of remote sensing in Earth system monitoring, particularly in coastal and marine environments. Her methodological blend of geophysics, signal processing, and artificial intelligence signals a paradigm shift in how seismic and geospatial data are interpreted. 🛰️🌍 Xinyue aims to develop robust and efficient systems that can handle real-world complexities with accuracy and computational elegance, making her research not only innovative but also essential for future environmental and energy challenges. 🌊🔎

🏅 Awards and Honors 

Xinyue Gong’s academic path has been adorned with recognition and accolades that mirror her exceptional talent and dedication. 🏆📜 Ranking 2nd out of 34 in her undergraduate program is a testament to her consistent excellence and intellectual drive. As the Project Leader in the National Innovation Training Program for Chinese College Students, she successfully led a multidisciplinary team to develop a deep learning-based coastline extraction system—a rare feat for an undergraduate researcher. 👩‍💻🌊 Her capability to manage complex datasets and lead innovation efforts was further recognized through her co-leadership in the Ocean Bottom Seismograph visual simulation project, which combined technical artistry with geophysical realism. 🎮🔬 Her work has not only brought national-level recognition but also forged a strong foundation for future scientific contributions. These accomplishments reflect not just skill, but a steadfast commitment to innovation and academic leadership in geoscience and computational modeling. 🧠💡

Publications Top Notes

Title: Compressed sensing approach to 3D spatially irregular seismic data reconstruction in frequency-space domain

Authors: Xinyue Gong, Shengchang Chen, Yawen Zhang, Ruxun Dou, Wenhao LuoFrontiers+1MDPI+1

Journal: Journal of Applied Geophysics

Year: 2025

DOI: 10.1016/j.jappgeo.2025.104345

Abstract: This study presents a novel method for reconstructing 3D spatially irregular seismic data by combining compressed sensing techniques with deep learning models in the frequency-space domain. The approach aims to enhance the accuracy and efficiency of seismic data reconstruction, which is crucial for subsurface imaging and geological interpretation.

🧾 Conclusion 

In a world increasingly defined by data and complexity, Xinyue Gong stands as a beacon of interdisciplinary brilliance. 🌟🔍 Her blend of geophysical expertise and AI-savviness is rare and impactful, enabling her to decode Earth’s secrets with precision and elegance. From theoretical frameworks to hands-on applications, she has demonstrated a comprehensive and forward-looking approach to scientific challenges. 🌐💻 Her leadership in national projects, proficiency in seismic data reconstruction, and passion for environmental understanding place her among the most promising young researchers in geosciences. As she continues her doctoral journey at Zhejiang University, she is poised not just to contribute—but to transform the field of geophysics. 🧪🌍 Xinyue Gong is not merely building a career; she is building bridges between Earth systems and intelligent computation, preparing to make waves in academia, industry, and beyond. 🚀🧬

Nevbahar Ekin | Applied Geophysics | Best Researcher Award

Ms. Nevbahar Ekin | Applied Geophysics | Best Researcher Award

Assoc. Prof. Nevbahar Ekin at Suleyman Demirel University, Turkey

Assoc. Prof. Nevbahar EKİN 🎓 is a dedicated geophysicist at Süleyman Demirel University 🇹🇷, specializing in Applied Geophysics and Electrical/Electromagnetic methods. With strong academic foundations from Ankara University and ongoing doctoral work at SDU 🏫, her research focuses on subsurface characterization, particularly the geophysical evaluation of concrete and geological structures. She actively contributes to scientific journals and projects backed by TÜBİTAK 📘🔬. Fluent in intermediate English 🌐, she enhances her professional skills through specialized courses in education management. Her commitment to advancing engineering technology through geophysical innovation makes her a respected figure in her field 🛰️💡.

Professional Profile:

Orcid

Scopus

Google Scholar

📚 Education and Experience 

  • 🎓 Doctorate in Geophysical Engineering – Süleyman Demirel University (Ongoing)

  • 🎓 Postgraduate in Geophysical Engineering – Ankara University (Ongoing)

  • 🎓 Undergraduate in Geophysical Engineering – Ankara University

  • 💼 Associate Professor – Süleyman Demirel University, Faculty of Engineering and Natural Sciences

💼 Professional Development 

Assoc. Prof. Nevbahar EKİN is continually enhancing her professional and academic growth 📈. She completed specialized training in Education Management and Planning and received language education at Hacettepe University in 2012 🏫🗣️. With a B1 level in English proficiency 🌍, she is actively involved in national and international research collaboration, leveraging her skills in geophysics for infrastructure assessment and geological mapping 🧲📊. Her practical knowledge is supported by courses that refine both her technical and pedagogical approach, making her a well-rounded academic with a clear vision for educational leadership and scientific impact 🎯📘.

🔬 Research Focus 

Assoc. Prof. Nevbahar EKİN’s research falls within Engineering and Technology, specifically Geophysical Engineering 🛠️🌍. She focuses on Applied Geophysics, using electromagnetic and electrical methods to analyze underground structures, concrete integrity, and geological formations 🔎🧲. Her work contributes to safer construction, infrastructure planning, and natural resource evaluation, bridging theoretical knowledge with real-world applications 🏗️📡. Through field surveys and data analysis, she aims to improve geophysical modeling and diagnostic accuracy. Her research is critical in understanding subsurface behavior, especially in civil engineering contexts, environmental monitoring, and natural hazard assessment 🌐⚡.

🏅 Awards and Honors 

  • 🏆 Publication Incentive Award (TÜBİTAK)Determination of Reinforced Concrete Strength by Electrical Resistivity, Journal of Applied Geophysics (October 2018)

  • 🏆 Publication Incentive Award (TÜBİTAK)Prediction of Reinforced Concrete Strength by Ultrasonic Velocities, Journal of Applied Geophysics (September 2017)

Publication Top Notes

  1. The Relationships Between Ultrasonic P and S Wave Velocities and Resistivity in Reinforced Concrete

    • Journal: Construction and Building Materials

    • Date: June 2025

    • DOI: 10.1016/j.conbuildmat.2025.141475

    • Summary: This article likely investigates the correlation between the ultrasonic P-wave and S-wave velocities and the resistivity of reinforced concrete. These parameters are typically used for non-destructive testing to assess the structural integrity of concrete, which can help in determining its strength and durability.

  2. BETON DAYANIMI TAHMİNİNDE İKİLİ VE ÇOKLU DOĞRUSAL REGRESYON ANALİZLERİNİN KARŞILAŞTIRILMASI

    • Journal: Mühendislik Bilimleri ve Tasarım Dergisi

    • Date: March 20, 2025

    • DOI: 10.21923/jesd.1572342

    • Summary: This paper compares the use of bivariate and multivariate linear regression models in predicting the durability of concrete. Durability is a crucial aspect of concrete’s long-term performance, and the analysis can help in optimizing concrete mixes and understanding their behavior under various environmental conditions.

  3. Düşük Dayanımlı Donatılı Betonlarda Donatının Sismik Hızlara Etkisi

    • Journal: Türk Deprem Araştırma Dergisi

    • Date: June 24, 2023

    • DOI: 10.46464/tdad.1269738

    • Summary: This article focuses on the effect of reinforcement on seismic velocities in low-strength reinforced concrete. It is relevant for earthquake-resistant design, as understanding the influence of reinforcement on seismic wave propagation can help improve the resilience of structures in seismic zones.

  4. Betonların Elastik Modül Hesabında Poisson Oranının Önemi

    • Journal: Journal of Advanced Research in Natural and Applied Sciences

    • Date: December 29, 2020

    • DOI: 10.28979/jarnas.845156

    • Summary: This paper discusses the importance of Poisson’s ratio in the calculation of the elastic modulus of concrete. Elastic modulus is a key property of concrete, affecting its stiffness and how it deforms under stress. Poisson’s ratio, which relates lateral strain to axial strain, is critical in these calculations.

  5. Comparison of Static and Dynamic Elastic Moduli in Concrete: Effects of Compressive Strength, Curing Conditions, and Reinforcement

    • Journal: Iranian Journal of Science and Technology – Transactions of Civil Engineering

    • Date: 2020

    • DOI: 10.1007/s40996-020-00513-7

    • Summary: This study compares static and dynamic elastic moduli in concrete, analyzing how compressive strength, curing conditions, and reinforcement affect these properties. Static and dynamic moduli offer insights into concrete’s behavior under different loading conditions.

  6. Prediction of Mechanical and Physical Properties of Some Sedimentary Rocks from Ultrasonic Velocities

    • Journal: Bulletin of Engineering Geology and the Environment

    • Date: 2019

    • DOI: 10.1007/s10064-019-01501-6

    • Summary: This article applies ultrasonic velocity measurements to predict the mechanical and physical properties of sedimentary rocks, offering parallels to similar approaches in concrete testing, where ultrasonic testing is used to assess material properties.

  7. Determination of the Reinforced Concrete Strength by Apparent Resistivity Depending on the Curing Conditions

    • Journal: Journal of Applied Geophysics

    • Date: 2018

    • DOI: 10.1016/j.jappgeo.2018.03.007

    • Summary: This study examines how apparent resistivity can be used to determine the strength of reinforced concrete, focusing on how curing conditions impact this relationship. Resistivity measurements are useful for evaluating the condition of concrete, especially in harsh environments.

  8. Prediction of Reinforced Concrete Strength by Ultrasonic Velocities

    • Journal: Journal of Applied Geophysics

    • Date: 2017

    • DOI: 10.1016/j.jappgeo.2017.04.005

    • Summary: This paper explores the use of ultrasonic velocities to predict the strength of reinforced concrete. Ultrasonic pulse velocity testing is a non-destructive method that can offer valuable insights into concrete’s internal structure and strength without the need for physical sampling.

Conclusion

Assoc. Prof. Nevbahar EKİN is a highly suitable candidate for a Best Researcher Award. Her work is innovative, interdisciplinary, and nationally recognized. By applying geophysical techniques to solve practical engineering problems, she exemplifies impactful research that transcends traditional disciplinary boundaries.